Interactions between man and machine as the basis of economics at every level

1/20. Your company's management is considering implementing artificial intelligence into the HR department. You are concerned that algorithms may reinforce existing biases. How do you respond?

Agree that it's not your problem since the technology works quickly and cheaply.

Allow the use of AI, but don't review the rules by which it makes decisions.

Introduce ethical review rules and create a team responsible for auditing AI decisions from the perspective of fairness.

Propose tests and analysis of the output to see if the algorithms reproduce stereotypes.

2/20. As you work on a new local service model, you see the growing presence of decision-support technology. How should you include humans in the process?

Integrate people and technology in a shared responsibility model.

Allow machines to act independently and humans to act as supervisors.

Reduce the human presence, since algorithms work faster.

Design a system in which humans complement the decisions made by machines.

3/20. You’re asked to evaluate how automation impacts job satisfaction. What angle do you prioritize in your analysis?

Survey staff but skip open feedback discussion.

Focus only on productivity and cost reduction.

Explore workflow changes and skill transitions.

Link tech use to meaning, motivation, and growth.

4/20. Your company is moving to automated production lines. Lower-level employees are full of anxiety about losing their jobs. How do you communicate this change to them?

Tell employees that they must adapt or, if they don't, they must leave.

Minimize the subject, hoping they will settle on their own.

Hold open discussions, offer reskilling programs and plan new career paths together.

Explain to them the role of technology and provide basic training.

5/20. Your enterprise is implementing new tools for analysis of data in real time, and managers expect results to be translated into decisions immediately. What attitude do you adopt?

Analyze data in the context of the company's goals, but leave room for human judgment.

Implement changes quickly, without checking the implications for teams.

Combine data analysis with team knowledge, involve different departments in interpreting the results, and only then make decisions.

Treat data as indisputable truth and automatically implement the results.

6/20. You're about to decide on a career specialty and are wondering what role machines will play in the future. How will you approach your career planning?

Assume that working with machines will limit your independence and creativity.

Build a career path based on your ability to collaborate with technology, strengthening your value as a human being in an automated world.

Choose a field where working with technology will allow you to operate more efficiently.

Focus on purely “human” occupations, avoiding technology industries.

7/20. In sales department, you notice that salespeople treat CRM as a control tool rather than a support tool. How do you change their attitude toward the technology?

Blindly ignore their resistance and enforce reporting without talking to them.

Encourage use of the system, but don't offer training or support.

Change the way of implementation: together with the team you match the tools to their work style, show their impact on customer relations and results.

Show the concrete benefits of automating tasks and saving time.

8/20. You've been asked to evaluate the effectiveness of a new algorithm that determines task allocation in your team. What is your approach to it?

Assume that since it's an algorithm, surely it always works objectively and without error.

Collect data, compare it with the team's opinions, identify patterns and make adjustments together with the tool's developers, ensuring the transparency of the process.

Check whether the distribution of tasks seems “fair” based on your own observations.

Examine whether the algorithm's decisions are repeatable and in line with the actual potential of employees.

9/20. Your logistics company is in the process of rolling out an AI-powered route optimization system for drivers. While the technology promises faster deliveries and fuel savings, several experienced drivers question the accuracy of the suggestions and feel their local knowledge is being ignored. You’ve been asked to increase trust in the system while ensuring efficiency gains. How do you proceed?

Explain that it will probably get better over time and hope for adoption.

Tell drivers to follow the system blindly and stop questioning it.

Facilitate hands-on training where drivers test and evaluate route options, give feedback, and adjust systems collaboratively.

Provide workshops comparing system recommendations with real-time outcomes.

10/20. A manufacturing client is preparing to integrate collaborative robots (cobots) into their production line. The leadership team wants rapid adoption, but workers on the floor are unclear about how their responsibilities will change. There are concerns about safety, job security, and skill fit. You’re responsible for guiding the rollout process. How do you proceed?

Install cobots immediately and let staff adjust on their own.

Design adaptive workflows where cobots support human skills, involve staff in planning, and include shared performance goals.

Recommend limiting cobot use to a single repetitive task.

Train workers to cooperate with cobots using basic safety routines.

11/20. Your cross-functional product development team has started using predictive analytics to guide decision-making. While some team members are enthusiastic, others resist relying on forecasts, preferring intuition and past experience. You’ve been asked to help improve buy-in and support better integration of data into creative workflows. What’s your plan?

Ignore resistance and proceed with the data model.

Enforce decisions based solely on analytics reports.

Show examples where data improved past decisions.

Create collaborative exercises where team members interpret predictions together and integrate them with lived expertise.

12/20. In response to increasing automation, your organization is revising job descriptions to reflect the evolving balance between human and machine tasks. Employees are uncertain about how their roles will change and what skills they need to develop. As the HR director, how do you proceed to ensure clarity and engagement?

Eliminate all tasks from job descriptions that could potentially be automated.

Collaborate with employees to co-create new role profiles that clearly define the synergy between human skills and technological capabilities.

Retain existing job descriptions and simply add “plus technology use” to each.

Analyse current responsibilities to identify areas where machines can assist, and update descriptions accordingly.

13/20. In response to the growing integration of artificial intelligence across various industries, you are tasked with developing a vocational training program centred on data analysis. The objective is to equip learners with the skills necessary to operate effectively in AI-enhanced environments, where collaboration between human expertise and machine intelligence is paramount. Recognizing the diverse backgrounds of your students and the evolving demands of the job market, how would you structure the program to best achieve this goal?

Create simulations where learners interpret AI-generated data and make informed decisions.

Emphasize mathematical theories and coding without context.

Focus solely on basic spreadsheet functions.

Include case studies showing AI's impact on business decisions.

14/20. Your retail company is transitioning from manual inventory checks to an advanced system utilizing smart sensors and AI algorithms. While this promises real-time stock updates and improved supply chain efficiency, many employees express concern about the reliability of the technology and fear that their roles may become redundant. As the operations manager, how do you address these concerns and facilitate a smooth transition?

Redefine job roles to emphasize human oversight, critical decision-making, and handling exceptions that the AI system may not address, positioning employees as essential partners in the technology integration.

Assign only tech-savvy employees to interact with the new technology.

Avoid discussing the new system to prevent alarming staff.

Provide comprehensive training focused on interpreting and responding to the system’s outputs.

15/20. The HR department in your company is deploying robotic process automation (RPA) to handle administrative tasks like leave approvals and performance tracking. While this should free up time for more meaningful work, many employees are worried about surveillance and losing control over their own data. You’ve been asked to introduce the system in a way that reduces fear and builds understanding. What’s your approach?

Send out a mass email announcing the change without context.

Launch it quietly and hope no one notices.

Hold a general briefing to explain its function and benefits.

Organize workshops explaining how it works, involve staff in process design, and ensure transparent communication from day one.

16/20. A smart work scheduling system is being implemented by your company. After the first weeks of operation, there are signs that some employees feel they are being treated unfairly. You are responsible for assessing the situation and making further decisions. How do you react?

Involve workers in reviewing the system, organize joint tests, identify gaps and make transparent changes together with the technical team.

Gather feedback from employees, analyze data from the system and suggest adjustments to schedules.

Assume that the system works according to the algorithm, so there is no need to explain anything to employees.

Explain workers that mistakes are possible, but you see no reason to make changes without the approval of the IT department.

17/20. You are responsible for AI governance in a public institution. How do you ensure AI systems make fair decisions for citizens?

Limit public transparency to protect intellectual property and avoid public misunderstanding of complex AI models.

Suspend all AI systems at the first sign of bias, regardless of societal or economic impact.

Rely on AI outputs for high-volume decisions and conduct periodic manual reviews only if public complaints arise.

Implement audit mechanisms, ensure explainability of AI decisions, involve interdisciplinary ethics committees and maintain human oversight in critical cases.

18/20. Urban planners want to use AI and IoT to create a smart city. For long-term sustainability, what should be prioritized?

Consult only technical experts, assuming civic participation would delay innovation.

Replace traditional public service roles with automated systems as a cost-saving priority, regardless of public sentiment.

Integrate privacy-by-design principles, establish transparent data governance and ensure citizen participation in decision-making.

Centralize citizen data collection to optimize services, while ethical decisions are postponed until systems are fully deployed.

19/20. Your country is adopting AI across public services. How do you make this transformation inclusive?

Prioritize digital innovation in high-income urban areas first, expanding to rural regions once systems are proven efficient.

Allow the market to determine which regions receive digital upgrades based on economic return.

Centralize AI development within government agencies without public consultation to maintain efficiency.

Ensure equitable access to digital infrastructure, invest in digital education, prevent algorithmic discrimination and engage diverse communities in policy-making.

20/20. You want to prepare your students or employees for the automated economy. Which approach best supports long-term employability?

Develop a balanced curriculum that combines AI/data literacy, critical thinking, creativity, ethical reasoning and continuous learning skills.

Limit digital education to basic computer use to avoid overwhelming learners with complex technologies.

Prioritize manual skills to preserve traditional employment sectors in case automation fails.

Focus on teaching discipline-specific theory, assuming adaptability can be learned later in the workplace.

Your result: /100

You have achieved a Low Readiness Index. Your approach to human–machine collaboration remains at an early stage. You often rely on automation without fully integrating your own judgment or critical interpretation of data. Ethical safeguards, bias prevention, and user participation in design are often missing, which can lead to mistrust and reduced adoption. In several areas, you use technology as a replacement rather than as a partner to human skills, overlooking opportunities for shared responsibility models and transparent communication. To improve, you should follow these steps.

Steps to be taken to improve your Readiness Index:

Prioritise embedding human oversight in all tech-driven processes.
Create hybrid workflows where machines enhance but do not dictate decisions.
Involve diverse stakeholders in design and testing.
Take targeted training to build confidence in interpreting and challenging AI outputs.
Strengthen communication to clearly explain how decisions are made and build user trust.

You have achieved a Moderate Readiness Index. Many of your responses indicate an awareness of the value of hybrid human–machine models, ethical reviews, and user inclusion, but these are not consistently selected. In some cases, you implement automation decisions without adequate feedback loops or cross-departmental input, and you use data without sufficient critical analysis. To strengthen your readiness, you should follow the steps below.

Steps to be taken to improve your Readiness Index:

Make ethics and bias audits standard practice.
Involve diverse teams early in project design.
Ensure training goes beyond technical operation to include strategic use of technology.
Introduce scenario-based simulations where teams jointly interpret AI outputs, compare them with human insights, and adjust processes accordingly.

You have achieved a High Readiness Index. Your results indicate strong ability to combine human insight with advanced technologies in a balanced and ethical way. You show confidence in interpreting data critically, recognising when human judgment should guide or adjust automated outputs, and maintaining transparency in decision-making. You also demonstrate skill in engaging stakeholders, designing solutions with user needs in mind, and integrating ethics into everyday practice. The scenario choices suggest you adapt quickly to change, approach automation as a partner rather than a replacement, and can manage both the technical and human dimensions of Industry 5.0. This readiness level confirms you are well prepared to operate in environments where collaboration between people and intelligent systems is central. To refine your skills even further, you should follow these steps:

Continue testing advanced personalisation in intelligent systems.
Explore multi-user collaboration tools.
Share best practices to strengthen wider readiness.

EQF level alignment

According to your results, your current competence level can be estimated as %EQF%.

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Interactions between man and machine as the basis of economics at every level